The loss of functional independence with aging has profound effects on older adults'quality of life and results in substantial social and economic burden to society at large. Disability prevention has emerged as a major focus of gerontology and geriatric medicine and an early functional decline represented by mobility disability has been the focus of our OAIC since its inception. However, disability prevention research has been hindered by measurement-related issues. For example, most assessment tools target "disability" or difficulty with a very basic set of tasks rather than "ability" across a broad range of task demands. This leads to ceiling effects and, in some instances, results in measures insensitive to the effects of interventions that are designed to enhance function. Also, the focus on disability rather .than "ability" has meant that we know very little about the large segment of the older population that is "well-functioning";i.e., those who do not report difficulties with basic functioning. Furthermore, most currently used self-report measures of mobility disability require participants to make complex judgments about the implicit meaning of apparently straightforward task descriptions such as walking 2-3 blocks. Yet, there are a number of contextual factors that are important in making such judgments. For example, is this inside or outside? Are there hills? How fast must I walk? Are there curbs or traffic? These ambiguities both add to the measure's variance and may limit the ability to compare results between different populations (e.g., rural and urban populations). To address these limitations, we propose to create and validate an innovative infrastructure that provides the capacity to assess mobility function and disability in a multimedia enhanced, and Computerized Adaptive Testing (CAT) environment, a measure we call M-CAT. The enhanced multimedia component makes extensive use of animation video clips.. Animation serves three purposes: First, it removes potential biases in judgments that may arise from characteristics such as the sex, race, age or experience of the actor. Second, it standardizes item interpretation. Respondents view the actual demands of the task and are no longer required to make implicit judgments regarding item content. For example, when asking about climbing a flight of stairs, we can present the task standardizing the speed, number of steps, light conditions and the presence or absence of handrails. And third, animation enables us to create a broad range of progressively difficult tasks in controlled settings by manipulating how the task is visually presented, an innovation in methodology that provides much finer discrimination of abilities and enables us to avoid ceiling and floor effects. We are able to capture the entire range of perceived ability of mobility from walking with a cane to jogging and walking over uneven terrain. The other component of the assessment environment, CAT, is a computer-based assessment technology that allows the customization of items (questionnaires) for each individual respondent. The computer scores every response from an individual and determines the best question to subsequently administer to that individual to efficiently determine his/her functional ability. With this improved ability to quickly "zoom in" on items that are appropriate for each individual, we can: (a) develop a measure that has a large number of items (e.g., >100) because individuals only respond to a subset of these questions based on their initial responses, (b) reduce response burden, (c) assess the mobility of older adults with a single instrument across all levels of ability, and (d) gain precision in measurement. We also expect that the correlation between scores on the M-CAT and actual performance measures of mobility disability will be higher than with widely-used measures due the enhanced precision of this measurement technology.